Advances in CNN performance have mainly come from architectural changes with the goals of easing gradient flow to deeper layers [1]}, [2]}, or reducing parameters per layer by restricting convolutional kernel size [3]} or their scope to only one dimension [4]}. Attention mechanisms for space or channel [5]} also seem to improve performance of CNNs, although it has not been explored why a stack of additional convolutional layers cannot model the same function as that of spatial or channel attention.
